Data is transmitted wirelessly every day. For example, a laptop may be wirelessly connected to a router which is in turn connected to a physical fiber. An end user sending an email may transmit the email wirelessly to the router, where it is then transferred to a fiber for transmission to its final destination.
Most wireless networks are based on various IEEE issued standards, including and not limited to, 802.11a, 802.11b, 802.11g and 802.11n. The current standard for wireless transfer of data is the 80211.ac standard which allows for a transfer speed of 1.75 GB its per second. This technology, like other radio-based technology uses a basic on/off binary transfer system.
One limitation of the binary system for the transmission of data is that there is a physical limitation to the transfer speed. For example, because binary is a series of bits represented as zeroes and ones, effective transfer speed requires billions of 0/1 bits per second. It is not possible to get such throughput in current technology. Improvements in the area of data transmission focus on increasing throughput as opposed to improving the efficiency of binary itself. There are currently no viable alternatives to binary transfer, and as a result, binary is ubiquitously employed.
In wireless data transmission, antennae are necessary to receive data. Antennas function by transmitting or receiving electromagnetic waves. An antennae is an electrical conductor assembled together with a power supply to increase the impedance in the antennae, causing metal conductors within to oscillate in a certain way that creates a radio frequency. By way of example, if a user wished to listen to FM radio, for example, channel 107.7, the antennae can be dialed to receive a frequency corresponding to the station number (107.7 MHz). Although the antenna can receive and is exposed to many frequencies of FM stations, in a radio, the user dials into one frequency to listen to the radio station on that frequency.
There is currently no system that uses variable frequencies to send the same data. As of the filing of this patent application, one of the fastest wireless transfers is Samsung 5g wireless, which is a couple billion GB/sec. The technology uses a different frequency band, but only one frequency (presently 60 GHz). There are also no viable alternatives to binary transfer, and as a result, binary is ubiquitously employed.
In addition, there are currently no systems which can compress a data file, regardless of its type. Current methods for data compression require knowledge of the information contained in a data file in order to remove redundancy in the data file. Compression schemes such as the zip format creates a codec which work on the application layer, not the physical layer (pure binary). For instance, a certain pixel or character (8 bit character, such as the letter “A”) can be given a code in arithmetic coding schemes such that the file can be compressed by replacing codes for certain characters or file parts. This is why there are various compression schemes for differing file types—because current compression systems require knowledge of the file's components in order to determine what will be redundant in the file (and therefore eliminated such that a compressed file can be generated). As a result, these systems can be very complex.
For instance, incoming binary code for an image is translated such that it is seen as an image with characteristics, such as pixels. After this interpretation, current compression schemes then operate on the file or folder. Previous compression systems will work, for example, by removing certain pixels and adding more noise to the image or overall quality of the image. Then, the processed image is assigned a new string of binary code, which is considered the compressed file. Compression of images or videos by these schemes can require complex algorithms and systems for determining which pixels may be redundant and therefore “eliminated” in a compressed file. Or, for example Huffman coding can be used to compress a text file, among other file types. The premise of this coding is that characters which repeat most often (for instance a space) are assigned a much shorter code of bits, so that when translated for transmission, the whole file is much shorter. Basically, the way these systems work is that they require knowledge of what the raw data stands for in order to decide what can be removed from a file for the purpose of compression.
Knowledge of data content for compression is seen in image compression, for example, when data is analyzed for statistical redundancy. For example, an image may have areas of color that do not change over several pixels; instead of coding each individual repeated pixel, the data may be encoded as “X number of red pixels.” Because a pixel is 16 bits, the analysis of the redundant pixels must therefore occur on the application layer, so to speak. Similarly, in the case of video, current compression schemes will see a video file and what it stands for. These compression systems may take into account such things as image quality and video size. Regarding the determination of statistical redundancy above, probability tables are sometimes used for the purpose of analyzing the probability that bit sequences down the string of a binary code may stand for something, for instance a pixel or certain character. There are several examples, but one is the use of probability schemes to determine if there is a high chance a certain upcoming byte (8 bit bit-pattern) will stand for something redundant, for example, the character (a byte) may be removed (or, in the case of an image pixel, a 16 bit section may be removed). Then this analyzed data may then be compressed so that redundant bytes are removed (or in the case of when an image pixel removed, a two byte string is removed) and then a new binary string is assigned to represent the original file. This new binary string is the compressed file which is transmitted. The probability schemes aim to ensure that the compressed file may be decompressed such that it is a close approximation of the original.
In the above example, the statistical apparatus of these systems are based solely on the type of data to be compressed, i.e., video or audio. Current compression systems do not operate on raw binary to compress simply a string of zeroes and ones. Instead, as alluded to above, these systems require an analysis of what the binary represents (the “data”) in order to compress the raw binary behind the data (meaning, operating from the standpoint of the application layer). The JPEG scheme, for example, does not simply take out zeroes and ones from the image's original binary code irrespective of what that bit might stand for.
Because different data types will have different properties in terms of what will be statistically redundant, current compression schemes are different for different data types. Images, text, audio or video will have different properties. For instance, while spaces may be most prevalent in a given text, certain colors might be most prevalent in an image. Therefore, those redundancies are handled differently based on the data type. Also, what is removed from a given original file is removed only after intelligent, and many times complex, analysis of redundancy in the original data.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. To the extent that work of the inventor hereof is described in this background section, as well as aspects of the invention that may not otherwise qualify as prior art at the time of filing, they are neither expressly nor impliedly admitted as prior art against the present disclosure.